The subject matter disclosed herein relates to industrial asset management, and more particularly, to monitoring and managing the health of an industrial asset using one or more autonomous robotic inspection systems.
Various entities may own or maintain various types of industrial assets as part of their operation. Such assets may include physical or mechanical devices or structures, which may, in some instances, utilize electrical and/or chemical technologies. Such assets may be used or maintained for a variety of purposes and may be characterized as capital infrastructure, inventory, or by other nomenclature depending on the context. For example, industrial assets may include distributed assets, such as a pipeline or an electrical grid, as well as individual or discrete assets, such as a wind turbine, airplane, a flare stack, vehicle, etc. Assets may be subject to various types of defects (e.g., spontaneous mechanical defects, electrical defects, or routine wear-and-tear) that may impact operation. For example, over time, an industrial asset may undergo corrosion or cracking due to weather or may exhibit deteriorating performance or efficiency due to the wear or failure of one or more component parts.
In some cases, a human inspector may inspect and analyze an industrial asset. For example, an inspector may look for and locate corrosion on the surface of an asset. However, depending on the location, size, and/or complexity of the asset and the surrounding environment, having one or more humans manually perform the inspection may take a substantial amount of time. Additionally, some inspection tasks may be boring, dirty, or otherwise unsuitable for a human. For example, some assets may have locations that are not easily accessible by humans due to height, confined spaces, danger, or the like.
To address these issues, one or more asset inspection robots equipped with sensors might be used to inspect an industrial asset. For example, a drone might be configured to fly in the proximity of an industrial flare stack taking pictures of various points of interest. Often, this will require one human operator to safely and effectively pilot the drone and another human operator to operate a sensor (e.g., a camera) to collect information about the asset. In another approach, an autonomous (or semi-autonomous) drone might follow a pre-determined flight path and/or make on-the-fly navigation decisions to collect data as appropriate. In some cases, however, it may be desirable to have a human monitor operation of an inspection robot (in case the robot runs into something it cannot handle itself, or the robot or inspection plan includes mistakes resulting in improper operation, etc.). Note that having a human review and/or monitor aspects of an inspection process can be a difficult and error-prone task. This can be especially true when a planned inspection will take a substantial amount of time, the inspection can potentially take various routes, there are many points of interest to be examined, the asset and/or surrounding environment are complex and dynamically changing, other people and/or robots are simultaneously operating in the area, etc.
It would therefore be desirable to provide systems and methods to facilitate situational awareness for an autonomous asset inspection robot monitor accurately and efficiently.